Vector quantization (VQ) has been applied to speech coding and image coding for a number of years. In its basic form, as illustrated in the block diagram of FIG. 1 for image coding, an image is divided into blocks of N.times.N picture elements or signal values (pixels). The encoder 2 and decoder 4 each have identical codebooks 6 and 8 containing various entries or vectors of signal values. The codebooks 6 and 8 are created by training them over a large set of particular test images of different A types. During the image coding process, a selected block of signal values Xi for the actual image being compressed is compared with entries 10 in the codebook 6 of the encoder 2, and the address of the entry 10 which best matches the image being compressed or encoded is transmitted to the decoder 4. The received address is used to fetch the corresponding entry 12 from the codebook 8, which is then used to reconstruct the selected block with signal values 1i for the image. For such a compression system, high quality performance is difficult to achieve, especially in the sharp transition areas of the image to be compressed.
Other vector quantization methods, such as the simplified VQ used by Comsat Labs, provide better quality but require a higher bit rate (i.e., a lower compression ratio). Moreover, these systems can exhibit a blocky-effect on the low transition areas of the image. Another compression technique uses a hierarchial codebook structure in which each level or hierarchy of codebooks corresponds to a different block size. The hierarchial approach provides good quality but also has a high bit rate due to the hierarchial codebook structure. A good review of vector quantization can be found in Gray, IEEE ASSP. Magazine, Apr. 1984, pp. 4-29.
Thus, a need exists for an improved VQ system for compressing a digital signal to produce a high quality signal from the compressed signal while maintaining a low bit rate and simple decoder design.